{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Basic Principles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Show different ways to present statistical data.\n",
    "\n",
    "This script is written in *MATLAB* or *IPpython* style, to show how\n",
    "best to use Python interactively.\n",
    "Note than in *IPython*, the ``show()`` commands are automatically generated.\n",
    "The examples contain:\n",
    "\n",
    "- scatter plots\n",
    "- histograms\n",
    "- KDE\n",
    "- errorbars\n",
    "- boxplots\n",
    "- probplots\n",
    "- cumulative density functions\n",
    "- regression fits\n",
    "\n",
    "Author: thomas haslwanter, March-2015"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Getting things ready"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "First, import the libraries that you are going to need. You could also do\n",
    "that later, but it is better style to do that at the beginning.\n",
    "pylab imports the numpy, scipy, and matplotlib.pyplot libraries into the\n",
    "current environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "sns.set_context('notebook')\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Generate data that are normally distributed\n",
    "x = randn(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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KkbtRoHZGiMqYDnLc+02YHgOZoeumNr3O/KO7VTYsKtYM9NNiCspt6Nayd/qd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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1419e664550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x,'.')\n",
    "title('Scatter Plot')\n",
    "xlabel('X')\n",
    "ylabel('Y')\n",
    "draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1419f9ab7f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ALEaRAwBgMYocAACLUeQAAFiMIgcAwGIUOQAAFqPIAQCwGEUOAIDFKHIAACzm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\n5AAAWOz/ASoPVr4l9tqpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1419e2f6208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(x)\n",
    "xlabel('Data Values')\n",
    "ylabel('Frequency')\n",
    "title('Histogram, default settings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1419fa780b8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tysrK0v3336/Zs2d7O5LbvfLKK2rdurUyMzOVnp6up556ytuRPGLSpEmaPn26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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1419fa0f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(x,25)\n",
    "xlabel('Data Values')\n",
    "ylabel('Frequency')\n",
    "title('Histogram, 25 bins')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### KDE\n",
    "\n",
    "Kernel Density Estimation (KDE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1419fb60f28>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g58aSp4bC16jD4dJmiFwLnjzcqIr69OnT8cMf/hB1dXW46aab8OijjyI5OXnE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\nc+2orO/Cpb6YqFBfLJwdgQWzIhAZwv3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1419fa14198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.kdeplot(x)\n",
    "xlabel('Data Values')\n",
    "ylabel('Density')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Cumulative probability density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1419e7d9198>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8Ej76bjcB/mb+dFNr/Mz6EI6Iuzn9K3znnXcoLCwkODiYtLQ0Xn/9dYqKigC4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1419fbc82b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numbins = 20\n",
    "cdf = stats.cumfreq(x,numbins)\n",
    "plot(cdf[0])\n",
    "xlabel('Data Values')\n",
    "ylabel('Cumulative Frequency')\n",
    "title('Cumulative probablity density function')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1419fb59e10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1419fc0d4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The error bars indicate 1.5* the inter-quartile-range (IQR), and the box consists of the\n",
    "# first, second (middle) and third quartile\n",
    "boxplot(x, sym='*')\n",
    "title('Boxplot')\n",
    "ylabel('Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1419fc860f0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PAQAHImeyAJBEZAEgicgCQBKRBYAkIgsASUQWAJI0qshWVlbGZZddFqNGjYqxY8fG1q1b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\n+X/JAMzlCDiW/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1419fbda588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot(x, vert=False, sym='*')\n",
    "title('Boxplot, horizontal')\n",
    "xlabel('Values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Errorbars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.2, 19)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WZclutxdZn5SUpBUrVigtLU09evTQmDFj3FkeAAAezWZZluXug2ZmZmrs2LEa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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1419fc6aac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = arange(5)\n",
    "y = x**2\n",
    "errorBar = x/2\n",
    "errorbar(x,y, yerr=errorBar, fmt='o', capsize=5, capthick=3)\n",
    "\n",
    "plt.xlabel('Data Values')\n",
    "plt.ylabel('Measurements')\n",
    "plt.title('Errorbars')\n",
    "\n",
    "xlim([-0.2, 4.2])\n",
    "ylim([-0.2, 19])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Check for Normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1419fe220b8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RmsZ498n8bJ2GGQuL6MNwZnCeCpc80sDLC9LT0TSkUqoUGNqTJk1iypQpnDhx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1419fbf1da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual check\n",
    "x = randn(100)\n",
    "_ = stats.probplot(x, plot=plt)\n",
    "title('Probplot - check for normality')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 2D Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x141a0073860>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7JAAQD/gzzx+Bjs6govUYaVxEyhTJ2V3stiMYCqOjq7vfc86rGCz64zzz/BH4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\nBm0iIiKTYNAmIiIyif8PQrmREkocA+oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x141a0068c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate data\n",
    "x = randn(200)\n",
    "y = 10+0.5*x+randn(len(x))\n",
    "\n",
    "# Scatter plot\n",
    "scatter(x,y)\n",
    "# This one is quite similar to \"plot(x,y,'.')\"\n",
    "title('Scatter plot of data')\n",
    "xlabel('X')\n",
    "ylabel('Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## LineFit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ag9ecjWWLZuKkk4rgLS9A9oHR72dH5XTMX74B85dvwBsPNwyd4MyoEh55TnTM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\n/weKmmFawHKDrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1419fc13f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M = vstack((ones(len(x)), x)).T\n",
    "pars = linalg.lstsq(M,y)[0]\n",
    "intercept = pars[0]\n",
    "slope = pars[1]\n",
    "scatter(x,y)\n",
    "plot(x, intercept + slope*x, 'r')\n",
    "show()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
